Efficient Transmission Power Control for Energy-harvesting Cognitive Radio Sensor Network

The rapid expansion of wireless sensor technology triggers several interesting applications. Given the small power capacity of a sensor, energy harvesting is an inevitable approach to extend the lifetime of the sensor nodes. In this paper, a distributed transmission power control mechanism for the energy harvesting cognitive radio sensor network (EH-CRSN) is proposed. The main concept is to adjust the transmission power of the nodes dynamically based on the network condition to maintain network connectivity. Each node decides to increase or decrease its transmission power dynamically based on several parameters such as its available power and neighboring nodes available power. This dynamic transmission power adjustment transforms the network logical topology to adjust with the power condition of network better. The transmission power control is tested in two scenarios; flat network and clustered network. Extensive simulation results show that by using of the proposed transmission power control method we can improve network end-to-end performance.

[1]  Anantha Chandrakasan,et al.  Dynamic Power Management in Wireless Sensor Networks , 2001, IEEE Des. Test Comput..

[2]  Roy D. Yates,et al.  A generic model for optimizing single-hop transmission policy of replenishable sensors , 2009, IEEE Transactions on Wireless Communications.

[3]  Alagan Anpalagan,et al.  Estimation of Distribution Algorithm for Resource Allocation in Green Cooperative Cognitive Radio Sensor Networks , 2013, Sensors.

[4]  Jan M. Rabaey,et al.  Energy aware routing for low energy ad hoc sensor networks , 2002, 2002 IEEE Wireless Communications and Networking Conference Record. WCNC 2002 (Cat. No.02TH8609).

[5]  Philipp Zhang,et al.  Preamble Design for Non-Contiguous Spectrum Usage in Cognitive Radio Networks , 2009, 2009 IEEE Wireless Communications and Networking Conference.

[6]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[7]  Brian M. Sadler,et al.  Opportunistic Spectrum Access via Periodic Channel Sensing , 2008, IEEE Transactions on Signal Processing.

[8]  Kin K. Leung,et al.  MAC Essentials for Wireless Sensor Networks , 2010, IEEE Communications Surveys & Tutorials.

[9]  Gerhard Fettweis,et al.  On Synchronization of Opportunistic Radio OFDM Systems , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[10]  Anantha P. Chandrakasan,et al.  An architecture for a power-aware distributed microsensor node , 2000, 2000 IEEE Workshop on SiGNAL PROCESSING SYSTEMS. SiPS 2000. Design and Implementation (Cat. No.00TH8528).

[11]  Sungsoo Park,et al.  Optimal mode selection for cognitive radio sensor networks with RF energy harvesting , 2012, 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC).

[12]  S. M. Heemstra de Groot,et al.  Power-aware routing in mobile ad hoc networks , 1998, MobiCom '98.

[13]  Özlem Durmaz Incel,et al.  QoS-aware MAC protocols for wireless sensor networks: A survey , 2011, Comput. Networks.

[14]  T. Weiss,et al.  Synchronization Algorithms and Preamble Concepts for Spectrum Pooling Systems , 2003 .

[15]  Xuemin Shen,et al.  Dynamic Channel Access to Improve Energy Efficiency in Cognitive Radio Sensor Networks , 2016, IEEE Transactions on Wireless Communications.

[16]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .